ヒューマノイド研究室は、連携大学院制度によって筑波大学メインキャンパスから約5kmの産総研にあるCNRS-AIST JRLに設置された筑波大学の研究室です。金広教授(連携大学院)の指導のもと、大学院生が技術研修生・RAとしてJRLに所属することができます。

この研究室では、大学院生が国内外の研究者とともに、さまざまなロボットを用いて様々な研究テーマに取り組むことができるユニークな機会を提供しています。主な研究テーマは、タスクや動作のプランニング、制御、人間や周辺環境とのマルチモーダルインタラクション、認知ロボティクスなどです。研究室のほとんどのメンバーはバイリンガルなので、日本語を話す学生だけでなく、英語を話す学生も研究室に参加することを推奨しています。

KANEHIRO Fumio
金広 文男

f-kanehiro_*_aist.go.jp

教授

当研究室では、常に優秀で意欲的な大学院生を募集しています。筑波大学大学院システム情報工学研究群知能機能システム学位プログラム修士課程または博士課程の入試(夏と冬に試験を実施)を受験し合格した方が参加できます。

興味のある方は、出願手続きを始める前に、研究室または金広教授に直接お問い合わせください。

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Vision-based Belt Manipulation by Humanoid Robot    
Deformable objects are very common around us in our daily life. Because they have infinitely many degrees of freedom, they present a challenging problem in robotics. Inspired by practical industrial applications, we present our research on using a humanoid robot to take a long, thin and flexible belt out of a bobbin and pick up the bending part of the belt from the ground. By proposing a novel non-prehensile manipulation strategy “scraping” which utilizes the friction between the gripper and the surface of the belt, efficient manipulation can be achieved. In addition, a 3D shape detection algorithm for deformable objects is used during manipulation process. By integrating the novel “scraping” motion and the shape detection algorithm into our multi-objective QP-based controller, we show experimentally humanoid robots can complete this complex task.  


sim2real: Learning Humanoids Locomotion using RL    

Recent advances in deep reinforcement learning (RL) based techniques combined with training in simulation have offered a new approach to developing control policies for legged robots. However, the application of such approaches to real hardware has largely been limited to quadrupedal robots with direct-drive actuators and light-weight bipedal robots with low gear-ratio transmission systems. Application to life-sized humanoid robots has been elusive due to the large sim2real gap arising from their large size, heavier limbs, and a high gear-ratio transmission systems.

In this work, we investigate methods for effectively overcoming the sim2real gap issue for large-humanoid robots for the goal of deploying RL policies trained in simulation to the real hardware.

The link to YouTube video is here.

 


Enhanced Visual Feedback with Decoupled Viewpoint Control in Immersive Teleoperation using SLAM  
During humanoid robot teleoperation, there is a noticeable delay between the motion of the operator’s and robot’s head. This latency could cause the lag in visual feedback, which decreases the immersion of the system, may cause some dizziness and reduce the efficiency of interaction in teleoperation since operator needs to wait for the real-time visual feedback. To solve this problem, we developed a decoupled viewpoint control solution which allows the operator to obtain the visual feedback changes with low-latency in VR and to increase the reachable visibility range. Besides, we propose a complementary SLAM solution which uses the reconstructed mesh to complement the blank area that is not covered by the real-time robot’s point cloud visual feedback. The operator could sense the robot head’s real-time orientation by observing the pose of the point cloud.  
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タイトル 著者 学会/論文誌 bib pdf
Learning to Classify Surface Roughness Using Tactile Force Sensors Y. Houhou, R. Cisneros-Limón, R. Singh IEEE/SICE International Symposium on System Integration 2024
Dual-Arm Mobile Manipulation Planning of a Long Deformable Object in Industrial Installation Y. Qin, A. Escande, F. Kanehiro, E. Yoshida IEEE Robotics and Automation Letters 2023
Mc-Mujoco: Simulating Articulated Robots with FSM Controllers in MuJoCo R. Singh, P. Gergondet, F. Kanehiro IEEE/SICE International Symposium on System Integration 2023
TransFusionOdom: Transformer-based LiDAR-Inertial Fusion Odometry Estimation L. Sun, G. Ding, Y. Qiu, Y. Yoshiyasu, F. Kanehiro IEEE Sensors Journal 2023
Learning Bipedal Walking for Humanoids with Current Feedback R. Singh, Z. Xie, P. Gergondet, F. Kanehiro IEEE Access 2023
Enhanced Visual Feedback with Decoupled Viewpoint Control in Immersive Humanoid Robot Teleoperation using SLAM Y. Chen, L. Sun, M. Benallegue, R. Cisneros-Limón, R. Singh, K. Kaneko, A. Tanguy, G. Caron, K. Suzuki, A. Kheddar, F. Kanehiro IEEE-RAS International Conference on Humanoid Robots 2022
Learning Bipedal Walking on Planned Footsteps for Humanoid Robots R. Singh, M. Benallegue, M. Morisawa, R. Cisneros-Limón, F. Kanehiro IEEE-RAS International Conference on Humanoid Robots 2022
CertainOdom: Uncertainty Weighted Multi-task Learning Model for LiDAR Odometry Estimation L. Sun, G. Ding, Y. Yoshiyasu, F. Kanehiro International Conference on Robotics and Biomimetics 2022
Visual SLAM framework based on segmentation with the improvement of loop closure detection in dynamic environments L. Sun, R. Singh, F. Kanehiro Journal of Robotics and Mechatronics 2021
Rapid Pose Label Generation through Sparse Representation of Unknown Objects R. Singh, M. Benallegue, Y. Yoshiyasu, F. Kanehiro IEEE International Conference on Robotics and Automation 2021
APE: A More Practical Approach To 6-Dof Pose Estimation A. Gabas, Y. Yoshiyasu, R. Singh, R. Sagawa, E. Yoshida IEEE International Conference on Image Processing 2020
Multi-purpose SLAM framework for Dynamic Environment L. Sun, F. Kanehiro, I. Kumagai, Y. Yoshiyasu IEEE/SICE International Symposium on System Integration 2020
Instance-specific 6-DoF Object Pose Estimation from Minimal Annotations R. Singh, I. Kumagai, A. Gabas, M. Benallegue, Y. Yoshiyasu, F. Kanehiro IEEE/SICE International Symposium on System Integration 2020
氏名 学年 メールアドレス
_*_を@に変えて送信してください
増田慎平 博士課程2年 masuda.shimpei_*_aist.go.jp
Cheng Hong 博士課程1年 cheng.hong_*_aist.go.jp
李 伊童 博士課程1年 li.cornel_*_aist.go.jp
西嶌 優 修士課程1年 y.nishijima_*_aist.go.jp
小野 祐 修士課程1年 ono.tasuku_*_aist.go.jp
青山 祐之介 修士課程1年 aoyama.y0093_*_aist.go.jp
Zenong Liu 修士課程1年 zenong.liu_*_aist.go.jp
Sunio Morisaki 研究生 sunio.morisakiromero_*_aist.go.jp
Rohan Pratap Singh 博士後期修了 rohan-singh_*_aist.go.jp
覃 毅力 博士後期修了
孫 楽源 博士後期修了 son.leyuansun_*_aist.go.jp
Xinchi Gao 博士前期修了
(since 03/2023)